Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks

• URL: http://arxiv.org/abs/2606.13941v1
• Date: Thu, 11 Jun 2026 22:05:33 GMT
• Title: Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks
• Abstract summary: Recent advances in Machine Learning and Deep Learning have sparked growing interest in their application to both signal detection and parameter estimation.<n>In this study, a hybrid Deep Learning strategy is proposed that leverages the effectiveness of Transformer encoders alongside well-established Convolutional Neural Network architectures.<n>The primary focus of this work is point estimation, producing single best-fit values for each parameter rather than full posterior distributions.
• Score: 0.27998963147546146
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe. Traditionally, modeled gravitational-wave signals have been identified using template-based matched filtering, followed by coincidence analysis across multiple detectors in the signal-to-noise ratio time series. Recent advances in Machine Learning and Deep Learning have sparked growing interest in their application to both signal detection and parameter estimation. In this study, a hybrid Deep Learning strategy is proposed that leverages the effectiveness of Transformer encoders alongside well-established Convolutional Neural Network architectures in an attempt to estimate the intrinsic and extrinsic parameters of non-precessing binary black hole systems. The primary focus of this work is point estimation, producing single best-fit values for each parameter rather than full posterior distributions. This method is evaluated on both simulated signals embedded in Gaussian noise and real gravitational-wave events, and it demonstrates strong predictive performance and robustness across key astrophysical parameters.

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